Evidence-theory-based collaborative spectrum sensing with efficient creditability evaluation in cognitive radio networks

Cognitive radio (CR), which enables opportunistic access to underutilized licensed bands, has been presented as a tempting technology for the optimization of spectrum utilization in recent years. To solve the hidden terminal problem, collaborative spectrum sensing (CSS), which benifits from spatial diversity, has been studied intensively in CR networks. For current CSS schemes, secondary users (SUs) are generally assumed to send sensing information honestly, however, misbehaved SUs may report false sensing data to mislead and compromise the system, which is called spectrum sensing data falsification (SSDF) attacks. In order to defense against malicious SSDF attacks, certain evidence-theory-based secure sensing approaches have been proposed. However, in most of the current evidence-theory-based CSS schemes, only real-time evidence information is utilized to estimate the sensing reliability of cognitive users, like the difference in SNR or similarity degree. Inadequate consideration causes CSS system being vulnerable to SSDF attacks, and the correctness of global decision making cannot be guaranteed. In this paper, we propose a credible CSS scheme based on evidence theory. It evaluates the credibility of each SU with two indexes, which are the statistical reputation factor and the real-time reliability factor, respectively. In addition, considering the transmitted data rises with the number of SUs increasing, we propose a rational mass distribution (RMD) approach to adjust evidence theory to binary hypothesis test in spectrum sensing context. As a consequence, both the data volume to be transmitted and the workload at data fusion center has been reduced. Simulation results have proved that the proposed scheme is effective and robust to defense against distinctive SSDF attack patterns.